code: def _get_iterator(inputs, distribution_strategy=None):
      if distribution_strategy:
          return distributed_training_utils_v1.get_iterator(
              inputs, distribution_strategy
          )
      return training_utils_v1.get_iterator(inputs)
  
imports: import functools
  import numpy
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.distribute import distributed_training_utils_v1
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils.generic_utils import make_batches
  from tensorflow.python.keras.utils.generic_utils import slice_arrays
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
label_desc: Use image node_id 6 for calling a global function with example usage: _get_iterator(inputs, distribution_strategy) and returns: training_utils_v1, distributed_training_utils_v1